The invention relates to a method of matching a variable two-dimensional image of a known three-dimensional object with a desired two-dimensional image of the object by step-wise changing the variable image in the case of non-correspondence between the images.
A method of this kind is known from the publication by Radu Horaud "New Methods for Matching 3-D Objects with Single Perspective Views", IEEE Transactions on Pattern analysis and Machine Intelligence, Vol. pami-9, No. 3, May 1987.
The invention also relates to a device for carrying out the method. According to the known method, a set of edges and corners representing a contour is used to match an image with different kinds of models. This method is used for determining spatial relationships between an object and a camera which is movable by means of a robot. According to the known method, small two-dimensional templates are matched with the actual image in order to extract positions of edges and corners. Subsequently, an investigation is started as to which object location could have produced this combination of corner and edge locations. Even though for two-dimensional object recognition the known method offers the advantage that the object location can still be found if the object is partly damaged or, if other objects are present in the image, parts of the object are masked by other objects, for the three-dimensional object recognition involving six degrees of freedom a number of problems are encountered when this method is used, so that this method is rejected for the latter application.
The "observation" angle and orientation of corners of the object are not known in advance. Because a perspective projection changes displayed corners with respect to the actual corners of the object, accurate and reliable determination of corner locations is intricate.
Corners and edges appear and disappear when the orientation of the object changes. For complex objects, for example, an object having a complex set of enclosed corners and edges such as an industrial tool used for assembly, the search process must examine an enormous amount of possibilities.
Projections of curved surfaces produce complex edges. Curved surfaces produce edges in images only due to the two-dimensional projection. The assumption of a simple image of edges in the image on physical edges on the object is not valid in this case.
Noise puzzles corner and edge detectors. Noise readily produces small variations which could be interpreted as corners. On the other hand actual corners could be neglected due to noise. This is due to the fact that the recognition process is only local, without surveying the overall image.
For example, when an image is out of focus due to large variations in the distance between object and the camera, projections of corners are rounded so that the corner detection process is impeded.
Finally, the projection may introduce fictitious corners. Notably the image of object parts situated at different distances from the camera may contain fictitious corners.